Summary: In Proceedings of UIST 2005, Seattle, WA, USA.
Preference Elicitation for Interface Optimization
Krzysztof Gajos and Daniel S. Weld
University of Washington
Seattle, WA 98195, USA
Decision-theoretic optimization is becoming a popular tool
in the user interface community, but creating accurate cost
(or utility) functions has become a bottleneck -- in most
cases the numerous parameters of these functions are cho-
sen manually, which is a tedious and error-prone process.
This paper describes ARNAULD, a general interactive tool
for eliciting user preferences concerning concrete outcomes
and using this feedback to automatically learn a factored cost
function. We empirically evaluate our machine learning al-
gorithm and two automatic query generation approaches and
report on an informal user study.
ACM Classification D.2.2 [Design Tools and Techniques]:
User Interfaces, H1.2. [Models and principles]: User/Machine